All SHP News News June 9, 2022

Investigation Into the ‘Dirty List’ of Slave Labor in Brazil Focus of Prize-Winning Thesis

Maria Clara Rodrigues worked with SHP's Grant Miller at the Stanford Human Trafficking Data Lab to uncover ways in which politically connected predators of human trafficking often avoid punishment.
Arial view of Rio de Janeiro, Brazil
An arial view of Rio de Janeiro, Brazil, (Agustin Diaz Gargiulo/Unsplash)

A senior working with the Stanford Human Trafficking Data Lab won both a Firestone Medal and the Kennedy Prize in the Social Sciences for her research documenting evidence that predators can skirt the “Dirty List of Slave Labor” in Brazil if they are politically connected or generous campaign donors.

Maria Clara Rodrigues (economics) is one of this year’s recipients of the Firestone Medal for Excellence in Undergraduate Research, awarded to the top undergraduate honors theses each year. She is also being honored with the Kennedy Prize in the Social Sciences. The Kennedy Thesis Prize is awarded annually to the single best thesis in each of the four areas of humanities and arts, social sciences, natural sciences, and engineering. Recipients of this award have accomplished exceptionally advanced research in the field and have shown strong potential for publication in peer-reviewed scholarly works.

Her research advisor, with whom she has worked closely for the past two years, is Grant Miller, Henry J. Kaiser, Jr. Professor and one of the faculty members at the data lab.

Maria Clara Rodrigues Stanford University

Brazil’s “Dirty List” is the principal policy tool used by the government to combat human trafficking. Individuals and companies placed on this list face financial penalties, are prohibited from obtaining credit through public banks, and suffer reputational harm due to the publicity generated around the list. But there are numerous reports of manipulation of the list by business elites who break trafficking laws in Brazil.

“Advising and collaborating with Clara over the past couple of years has been an inspiring experience, one through which I’ve personally learned a great deal," said Miller, a core faculty member at Stanford Health Policy. "Clara is a Brazilian citizen herself, and she has dedicated herself intensively to conducting new, groundbreaking research on this critically important topic. At the outset, Clara immersed herself in the messy and laborious details of obtaining, cleaning, and thoroughly understanding the government administrate data sources that would provide the foundation for her thesis research.”

Rodrigues then linked this information at the individual and company level to records from the Dirty List itself, then matched these data sources to publicly available information on campaign contributions and the political affiliations and records of the recipients of these contributions.

“Ultimately, she produced a fully linked and cleaned data resource enabling her to track individuals and companies consistently across them in a standardized way,” Miller said. “Clara then used this remarkable new data resource to study how individuals and companies in Brazil found to violate human trafficking laws manipulate, and even subvert, the Dirty List process to advance their private interests.”

Rodrigues also recommended policy and institutional modifications to the Dirty List.

“The current scant knowledge of labor traffickers’ relationship with government institutions, regulatory agencies, and incumbents suggest that the mechanisms, incentives, and dynamics of human trafficking will remain in force and continue affecting millions of people worldwide,” she wrote in the conclusion of her thesis. “Dedicating research efforts to disentangle the modus operandi of perpetrators will enable analysts and decision-makers to better predict their behavior and tackle trafficking more effectively.”

The trafficking lab conducts critical research through a collaboration among academics, health-care providers and frontline trafficking experts and prosecutors, using the tools of modern social science and data science.

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